**Replication Data

**Selling Them Short?
**Nichole Bauer, Tatum Taylor
**Political Research Quarterly

use sellingshort_prq_replication.dta

**Variables 
**political = political experience in news coverage
**professional = profesisonal experience in news coverage
**academic = academic experience in news coverage
**feminine = feminine stereotypes in news coverage
**qual_type = categorical variable with political, professional, academic, feminine qualifications
**mixed_gender = woman vs. man races
**all_women = woman vs. woman races
**all_men = man vs. man races
**compete = competetitive senate race
**openesat = no incumbent in the senate race
**femaleauthor_prop = proportion of authors writing a news article who are women
**supernational = national news outlets
**fips = State level variable that matches Federal Information Processing Code
**femalecand = Female Canddiate
**Democrat = Democratic candidate
**incumbent = Incumbent candidate
**paracandidate_prop = proportion of paragraphs in a news article that discuss the senate candidate
**state_elected = currently or previously held statewide elected office
**house_member = currently or previously held a US House seat
**stateleg = currently or previously served in state legislature
**masculinejob = held a masculine stereotyped job
**femininejob = held a feminine stereotyped job
**bachelors = candidate holds a bachelors degree
**graduate = candidate holds a graduate degree
**ivyleague = candidate holds a degree, bachelors or graduate, from an Ivy League university
**married = candidate currently married
**children = candidate has children
**article_num = unique identifier for each article

**Mixed-Gender, Same Gender Race Comparisons

**% for Table 4 Frequencies

tab political if mixed_gender==1
tab professional if mixed_gender==1 
tab feminine if mixed_gender==1

tab political if all_men==1
tab professional if all_men==1
tab academic if all_men==1
tab feminine if all_men==1

tab political if all_women==1
tab professional if all_women==1
tab academic if all_women==1
tab feminine if all_women==1

**Models for Web Appendix 4, Table A4, Figure 1 in main text
mlogit qual_type mixed_gender all_men compete openseat femaleauthor_prop supernational , cluster(fips)

margins, at (mixed_gender=1) predict(outcome(1))
margins, at (mixed_gender=1) predict(outcome(2))
margins, at (mixed_gender=1) predict(outcome(3))
margins, at (mixed_gender=1) predict(outcome(4))

margins, at (all_men=1) predict(outcome(1))
margins, at (all_men=1) predict(outcome(2))
margins, at (all_men=1) predict(outcome(3))
margins, at (all_men=1) predict(outcome(4))

margins, at (all_men=0 mixed_gender=0) predict(outcome(1))
margins, at (all_men=0 mixed_gender=0) predict(outcome(2))
margins, at (all_men=0 mixed_gender=0) predict(outcome(3))
margins, at (all_men=0 mixed_gender=0) predict(outcome(4))


**Models for Figure 2 in main text, and Web Appendix 4, Table A5 

logit political femalecand democrat incumbent  compete openseat femaleauthor_prop supernational paracandidate_prop state_elected house_member  localoffice stateleg masculinejob femininejob bachelors graduate ivyleague married children if mixed_gender==1, cluster(fips)

logit professional femalecand democrat incumbent  compete openseat femaleauthor_prop supernational paracandidate_prop state_elected house_member  localoffice stateleg masculinejob femininejob bachelors graduate ivyleague married children if mixed_gender==1, cluster(fips)

logit academic femalecand democrat incumbent  compete openseat femaleauthor_prop supernational paracandidate_prop state_elected house_member  localoffice stateleg masculinejob femininejob bachelors graduate ivyleague married children if mixed_gender==1, cluster(fips)

logit feminine femalecand democrat incumbent  compete openseat femaleauthor_prop supernational paracandidate_prop state_elected house_member  localoffice stateleg masculinejob femininejob bachelors graduate ivyleague married children if mixed_gender==1, cluster(fips)


**Models for Figure 3 in main text and Web Appendix 4, Table A6
logit political all_women democrat incumbent  compete openseat femaleauthor_prop supernational paracandidate_prop state_elected house_member localoffice stateleg masculinejob femininejob bachelors graduate ivyleague married children if mixed_gender==0, cluster(fips)

logit professional all_women democrat incumbent  compete openseat femaleauthor_prop supernational paracandidate_prop state_elected house_member localoffice stateleg masculinejob femininejob bachelors graduate ivyleague married children if mixed_gender==0, cluster(fips)

logit academic all_women democrat incumbent  compete openseat femaleauthor_prop supernational paracandidate_prop state_elected house_member localoffice stateleg masculinejob femininejob bachelors graduate ivyleague married children if mixed_gender==0, cluster(fips)

logit feminine all_women democrat incumbent  compete openseat femaleauthor_prop supernational paracandidate_prop state_elected house_member localoffice stateleg masculinejob femininejob bachelors graduate ivyleague married children if mixed_gender==0, cluster(fips)


